Systems and methods for identifying a source of radio frequency interference in a wireless network

ABSTRACT

An interference detection system in a network determines that an unknown radio frequency (RF) interference source that causes RF interference experienced by a first wireless station is a persistent RF interference source over a plurality of time intervals in a selected time period. A predicted interference source location is identified for each time interval in the selected time period. An aggregated predicted interference source location is calculated based on the identified one or more predicted interference source locations.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority to and is a continuation of U.S.patent application Ser. No. 17/587,344, filed Jan. 28, 2022 and entitled“Systems and Methods for Identifying a Source of Radio FrequencyInterference in a Wireless Network,” which is a continuation-in-part ofU.S. patent application Ser. No. 16/896,323, filed Jun. 9, 2020, andentitled “Systems and Methods for Identifying a Source of RadioFrequency Interference in a Wireless Network,” the entirety of which arehereby incorporated by reference herein.

BACKGROUND

Wireless telecommunications networks may operate on portions of theradio frequency (RF) spectrum. In some situations, interference may becaused in such a way that is detrimental to the performance of a givenwireless telecommunications network. For example, external interferencemay occur when a device external to the network site transmits a signalin a spectrum that overlaps the RF spectrum of the network. In someinstances, interference events are irregular, affecting sites on aparticular day of the week or specific business hours, which can make itdifficult to identify the cause or source of the interference.Furthermore, the conventional process for identifying a source ofinterference requires significant human capital and specializedequipment. For example, even after field engineers manage to determinethat an interference event is occurring or has occurred for a particularnetwork site, the engineers must physically canvass the area proximateto the network site with a directional antenna to identify fluctuationsof the interference levels until the source of the interference isidentified.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an overview of an environment in which systems andmethods consistent with embodiments are used;

FIG. 2 illustrates an example environment in which one or moreembodiments, described herein, may be implemented;

FIG. 3 is a block diagram illustrating example components of a computerdevice 400 according to one embodiment;

FIG. 4 is a flow diagram illustrating an example process for estimatinga location of an unknown interference source, consistent withimplementations described herein;

FIG. 5 is a graph of exemplary uplink radio power for a wireless stationon a per-physical resource block (PRB) basis;

FIG. 6 illustrates an exemplary main wireless station and a number ofneighboring wireless stations;

FIG. 7 is a flow diagram illustrating one implementation of a processfor identifying candidate interference source locations consistent withembodiments described herein;

FIG. 8 is a graphical depiction of an exemplary boundary selection forthe example of FIG. 6 ;

FIG. 9 illustrates an exemplary heat map based on the example of FIG. 6;

FIG. 10 is a view of a portion, of the map of FIG. 8 , illustratingsector boundaries for a wireless station;

FIG. 11 is a flow diagram illustrating one implementation of a processfor sector analysis vector generation consistent with embodimentsdescribed herein;

FIG. 12 is a flow diagram illustrating one implementation of a processfor refining a predicted interference source location based onhistorical data, consistent with embodiments described herein;

FIG. 13 ; is a flow diagram illustrating one implementation of a processfor further refining a predicted interference source location; and

FIGS. 14A and 14B are graphical depictions of a trilaterationmethodology consistent with implementations described herein.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following detailed description refers to the accompanying drawings.The same reference numbers in different drawings may identify the sameor similar elements. The following detailed description does not limitthe invention, which is defined by the claims.

Telecommunications service providers may operate wireless networks(e.g., cellular or other types of wireless networks) at a given set offrequencies (or frequency bands) of the Radio Frequency (RF) spectrum.While these frequencies are often licensed (e.g., by a governmentalagency and/or by some other authority) for exclusive use by one entityor operator, some bands may be shared by multiple different entities.For instance, a portion of the RF spectrum may be designated for “sharedaccess,” or a portion of the RF spectrum that was previously licensedfor access by one entity may be licensed for additional entities. Insituations where the same portion of the RF spectrum is licensed for useby multiple entities, the use of the portion of the RF spectrum by oneentity may negatively impact the use of that portion of the RF spectrumby other entities.

For example, and as shown in FIG. 1 , an entity may cause excessive RFinterference (referred to herein simply as “interference” or “noise”).In some cases, there may be multiple sources causing RF interference.For example, assume that a wireless network provider operates wirelessstations 100-1 to 100-2 within a particular frequency band, and thatwireless stations 100 services a user equipment device (UE) 105. Furtherassume that another device 110 (also referred to as broadcast source110), which is associated with another entity, also operates within thesame frequency band, and emits an RF interference signal into that bandbecause of intermodulation, excessive power, poor filter design, or forother reasons. Such third-party broadcast sources may negatively impactthe operation of wireless station 100 (and/or of UE device 105 thatcommunicate with wireless station 100, such as mobile telephones,Internet of Things (“IoT”) devices, Machine-to-Machine (“M2M”) devices,etc.), by introducing RF interference or noise. Because the broadcastsource is associated with an entity that is separate from the entitythat owns and operates wireless stations 100, it may be difficult tocoordinate the operation of wireless station 100 to account for theunexpected and unpredictable interference caused by the broadcastsource.

Consistent with implementations described herein, an interference sourcelocation determination tool may be provided to more quickly andaccurately identify a likely location of an interference source. Inparticular, interference may be determined based on a particularfrequency range within which it is occurring. Wireless stations 100 areconfigured to operate in accordance with various frequency bands andtime slots, arranged in physical resource blocks (PRBs). A PRB denotesthe most granular aspect of a wireless station's capabilities andincludes both a frequency component and a time component. As describedherein, interference may be experienced and analyzed on a per-PRB basis.

For example, as described herein, interference-indicating data, such asuplink power measurements data (i.e., uplink interference levelmeasurements) for particular PRBs may be received and stored by thewireless stations 100. When a wireless station experiences externalinterference, interference-indicating data for the neighboring wirelessstations are retrieved and analyzed to determine whether similarinterference is perceived by any neighboring wireless stations. Oncewireless stations that are not experiencing a similar externalinterference are filtered out, a heat map indicating a likelylocation(s) of the interference source may be generated.

For example, when an affected wireless station is 100 is identified(referred to herein as main wireless station 100-1), either autonomouslyby an interference detection system or via external (e.g., manual)reporting, other wireless stations 100 that are proximate (i.e.,geographic neighbors) to the main wireless station 100-1 are examinedfor similar interference experiences on a particular PRB or PRBsaffecting main wireless site 100-1.

Consistent with embodiments described herein, the likely location(s) maybe determined by calculating error, such as root mean square error(RMSE) by using Free Space Path Loss (FSPL) calculations based on anumber of interference source location guesses. The process isiteratively repeated until minimum values of FSPL are determined. Theheat map is generated based on the calculations for each of a number ofguessed locations. In one implementation, the generated heat map isprovided to field engineers to assist in expediting manualidentification of the interference source. In other implementations, thegenerated heat map may be forwarded to an autonomous identificationsystem (e.g., an artificial intelligence-based system) to investigateand identify the interference source. For example, the autonomousidentification system may deploy one or more autonomous vehicles (e.g.,automobiles, unmanned aerial vehicles, etc.) based on the generated heatmap.

FIG. 2 illustrates an example environment in which one or moreembodiments, described herein may be implemented. As shown in FIG. 2 ,environment 200 may include radio access network (RAN) 205 that includesa plurality of wireless stations 100-1 to 100-x (collectively referredto as wireless stations 100 and individually referred to as wirelessstation 100), a wireless station database 210, an interference detectionsystem 215, an interference reporting system 220, and one or morenetworks 225. The number of devices and/or networks, illustrated in FIG.2 , is provided for explanatory purposes. In practice, environment 200may include additional, fewer, different, or a different arrangement ofdevices and/or networks than illustrated in FIG. 2 .

For example, while not shown, environment 200 may include devices thatfacilitate or enable communication between various components shown inenvironment 200, such as routers, modems, gateways, switches, hubs, etc.Alternatively, or additionally, one or more of the devices ofenvironment 200 may perform one or more functions described as beingperformed by another one or more of the devices of environments 200.Devices of environment 200 may interconnect with each other and/or otherdevices via wired connections, wireless connections, or a combination ofwired and wireless connections. In some implementations, devices ofenvironment 200 may be physically integrated in, and/or may bephysically attached to other devices of environment 200.

RAN 205 may include a wireless telecommunications network (e.g., aLong-Term Evolution (LTE) RAN, a Third Generation Partnership Project(3GPP) a Fifth Generation (5G) RAN, etc. As mentioned above, RAN 205 mayinclude one or more wireless stations 100, via which devices (e.g., userequipment (UE), such as mobile telephones, IoT devices, M2M devices,etc.) may communicate with one or more other elements of environment200. RAN 205 may communicate with such devices via an air interface. Forinstance, RAN 205 may receive traffic (e.g., voice call traffic, datatraffic, messaging traffic, signaling traffic, etc.) from a UE via theair interface, and may forward the traffic to network 225. Similarly,RAN 205 may receive traffic intended for a UE from network 225 and mayforward the traffic to the UE via the air interface. RAN 205 may operateat a set of frequencies (e.g., a set of licensed spectra). In someembodiments, one or more of the bands, at which RAN 205 operates, may beshared with an entity other than the entity that owns and/or operatesRAN 205.

Wireless station database 210 may include one or more devices (e.g., aserver device, or a collection of server devices) for storing wirelessstation-related information. For example, wireless station database 210may receive, store, and/or output information relating to variouswireless stations 100 in RAN 205. Such information may include, amongother data elements, identification information, geographic locationinformation, and performance information relating to performancecharacteristics of each wireless station 100.

Interference detection system 215 may include one or more devices (e.g.,a server device, or a collection of server devices) to determine likelylocations of interference sources. For example, interference detectionsystem 215 may identify likely locations of interference sourcesdetected in RAN 205. For example, as briefly described above,interference detection system 215 may generate geographic heat maps thatidentify the likely locations of sources of interference based on datacollected from wireless stations 100 within RAN 205. Consistent withembodiments described herein, the heat map may be generated based onstatistical minimization of free space path loss calculations at variousgeographic locations proximate to affected wireless stations.Interference detection system 215 may further take administrative orcorrective actions when detecting unique sources of interference, asdescribed in greater detail below.

Interference reporting system 220 may include one or more devices (e.g.,a server device, or a collection of server devices) to perform one ormore functions described herein. For example, interference reportingsystem 220 may include messaging systems capable of generating and/orsending messages via network 225. The messages may be emails, textmessages, application-specific messages, and/or other types of messagesrelated to alerts that a heat map of possible interference sources hasbeen generated by interference detection system 215. Consistent withimplementations described herein, interference reporting system 220 mayforward or otherwise notify network personnel (e.g., field engineers)about the identified interference and the generated heat map for use inascertaining the source of the interference. Interference reportingsystem 220 may also maintain a history of interference determinationsfor use in determining patterns.

Network(s) 225 may include one or more wired and/or wireless networks.For example, network(s) 225 may include one or more core networks of alicensed wireless telecommunications system (e.g., an LTE core network,a 5G core network, etc.), an Internet Protocol (“IP”)-based PDN, a widearea network (“WAN”) such as the Internet, a private enterprise network,and/or one or more other networks. One or more of the devices ornetworks shown in FIG. 2 may communicate, through network(s) 225, witheach other and/or with other devices that are not shown in FIG. 2 .Network 225 may further include, or be connected to, one or more othernetworks, such as a public switched telephone network (“PSTN”), a publicland mobile network (“PLMN”), and/or another network.

FIG. 3 is a block diagram illustrating example components of a computerdevice 300 according to one embodiment. Wireless stations 100, wirelessstation database 210, interference detecting system 215, andinterference reporting system 220 may include or may be included withinone or more of computer device 300. As shown in FIG. 3 , computer device300 may include a bus 310, a processor 320, a memory 330, an inputdevice 340, an output device 350, and a communication interface 360.

Bus 310 includes a path that permits communication among the componentsof computer device 300. Processor 320 may include any type ofsingle-core processor, multi-core processor, microprocessor, latch-basedprocessor, and/or processing logic (or families of processors,microprocessors, and/or processing logics) that executes instructions.In other embodiments, processor 320 may include an application-specificintegrated circuit (ASIC), a field-programmable gate array (FPGA),and/or another type of integrated circuit or processing logic.

Memory 330 may include any type of dynamic storage device that may storeinformation and/or instructions, for execution by processor 320, and/orany type of non-volatile storage device that may store information foruse by processor 320. For example, memory 330 may include a randomaccess memory (RAM) or another type of dynamic storage device, aread-only memory (ROM) device or another type of static storage device,a content addressable memory (CAM), a magnetic and/or optical recordingmemory device and its corresponding drive (e.g., a hard disk drive,optical drive, etc.), and/or a removable form of memory, such as a flashmemory.

Input device 340 may allow an operator to input information into device300. Input device 340 may include, for example, a keyboard, a mouse, apen, a microphone, a remote control, an audio capture device, an imageand/or video capture device, a touch-screen display, and/or another typeof input device. In some embodiments, device 300 may be managed remotelyand may not include input device 340. In other words, device 300 may be“headless” and may not include a keyboard, for example.

Output device 350 may output information to an operator of device 300.Output device 350 may include a display, a printer, a speaker, and/oranother type of output device. For example, output device 350 mayinclude a display, which may include a liquid-crystal display (LCD) fordisplaying content to the customer. In some embodiments, device 300 maybe managed remotely and may not include output device 350. In otherwords, device 300 may be “headless” and may not include a display, forexample.

Communication interface 360 may include a transceiver that enablesdevice 300 to communicate with other devices and/or systems via wirelesscommunications (e.g., radio frequency, infrared, and/or visual optics,etc.), wired communications (e.g., conductive wire, twisted pair cable,coaxial cable, transmission line, fiber optic cable, and/or waveguide,etc.), or a combination of wireless and wired communications.Communication interface 360 may include a transmitter that convertsbaseband signals to RF signals and/or a receiver that converts RFsignals to baseband signals. Communication interface 360 may be coupledto one or more antennas/antenna arrays for transmitting and receiving RFsignals.

Communication interface 360 may include a logical component thatincludes input and/or output ports and/or other input and outputcomponents that facilitate the transmission of data to other devices.For example, communication interface 360 may include a network interfacecard (e.g., Ethernet card) for wired communications and/or a wirelessnetwork interface (e.g., a WiFi) card for wireless communications.Communication interface 360 may also include a universal serial bus(USB) port for communications over a cable, a Bluetooth wirelessinterface, a radio-frequency identification (RFID) interface, anear-field communications (NFC) wireless interface, and/or any othertype of interface that converts data from one form to another form.

Device 300 may perform various operations in response to processor 320executing software instructions contained in a computer-readable medium,such as memory 330. A computer-readable medium may be defined as anon-transitory memory device. A memory device may be implemented withina single physical memory device or spread across multiple physicalmemory devices. The software instructions may be read into memory 330from another computer-readable medium or from another device. Thesoftware instructions contained in memory 330 may cause processor 320 toperform processes described herein. Alternatively, hardwired circuitrymay be used in place of, or in combination with, software instructionsto implement processes described herein. Thus, implementations describedherein are not limited to any specific combination of hardware circuitryand software.

Although FIG. 3 shows exemplary components of device 300, in otherimplementations, device 300 may include fewer components, differentcomponents, additional components, or differently arranged componentsthan depicted in FIG. 3 . Further, in some embodiments, one or more ofthe components described above may be implemented as virtual components,such as virtual processors, virtual memory, virtual interfaces, etc.Additionally, or alternatively, one or more components of device 300 mayperform one or more tasks described as being performed by one or moreother components of device 300.

FIG. 4 illustrates an example process 400 for estimating a location ofan unknown interference source, consistent with implementationsdescribed herein. In some embodiments, process 400 may be performed byinterference detection system 215. In some embodiments, process 400 maybe performed by, or in conjunction with, one or more other devices orsystems, such as wireless station database 210, and/or interferencereporting system 220. FIG. 4 is described in conjunction with FIGS. 5-10. Some of these figures include graphs or other graphicalrepresentations of data, which may be generated by interferencedetection system 215. In some embodiments, the figures graphicallyillustrate calculations, aggregation, analysis, and/or other types ofoperations that may be performed by interference detection system 215.

Process 400 may include identifying one or more wireless stations thatare experiencing unexpected interference, particularly when compared tosurrounding wireless stations (block 405). Consistent with embodimentsdescribed herein, interference may be determined based on a particularfrequency range within which it is occurring. Wireless stations 100 areconfigured to operate in accordance with various frequency bands andtime slots, arranged in physical resource blocks (PRBs). A PRB denotesthe most granular aspect of a wireless station's capabilities andincludes both a frequency component and a time component. For long termevolution (LTE) wireless stations (e.g., eNodeB's) or 5G New Radio (5G)wireless stations (e.g., gNodeB's), each wireless station 100 may have aset number of PRBs across its available frequency spectrum, each ofwhich comprise approximately 180 KHz of bandwidth. Accordingly, for awireless station 100 operating in a 10 MHz band, the wireless stationwill generally include 50 PRBs, each having a discrete frequency andtime allocation. Thus, for a given sector (e.g., where “sector” refersto a particular geographic region, which may approximately or preciselycorrespond to the coverage area of a particular wireless station 100, ora set of wireless stations 100, of RAN 205) and over a given time window(e.g., one minute, one hour, one day, one week, etc.), the received(i.e., uplink) radio power, per PRB, may be measured or otherwiseretrieved.

For instance, FIG. 5 includes a graph that shows an example of uplinkradio power, on a per-PRB basis, at a given sector and within a giventime window. Each bar on the plot may indicate, in some embodiments, anaverage of the received uplink radio power measured over a time window.In some embodiments, the plot may indicate different aspects of thereceived radio power (e.g., the maximum uplink radio power measured overthe time window, the minimum uplink radio power measured over the timewindow, etc.). As shown, the uplink radio power measured at PRB 6 andPRB 10, at the sector and over the time window, may be relatively high,as compared to the radio power at the other PRB s. The relatively highuplink radio power may indicate a likely interference event.

Consistent with implementations described herein, PRB uplinkinterference level measurements or other related measurements forwireless stations 100 may be aggregated or otherwise maintained inwireless station database 210 on a periodic basis, such as every minute,every 10 minutes, every hour, etc. For example, wireless stations 100may be configured to report various elements of performance metrics(i.e., key performance indicators (KPIs)) on a periodic basis. Thereported KPIs may include uplink power level measurements for each PRBin the wireless station 100. Interference detection system 215 maymonitor the PRB uplink power measurements for each wireless station 100and may determine instances of likely interference based thereon. Forexample, continued disrupted PRB uplink power measurements over a periodof time (e.g., three days, etc.) may be a strong indication of aconsistent external interference source. In one example, this may beidentified by uplink interference levels of greater than −110 dBm onmildly affected wireless stations and uplink interference levels ofapproximately −80 dBm for more heavily affected sites. By comparinguplink interference level measurements over a period of time on eachPRB, the existence of interference effects on each wireless station maybe determined,

In some embodiments, autonomous systems, such as artificial intelligenceor machine learning systems may be implemented in interference detectionsystem 215 to identify interference-experiencing wireless stations 100based on the available historical data. In other implementations,interference detection system 215 may receive indications ofinterference experiencing wireless stations 100 via a manual reportingsystem. For example, wireless interference detection system may receivea wireless station identifier and date/time of the interference from anoperator.

When an affected wireless station 100 is identified (e.g., wirelessstation 100-1), either autonomously by interference detection system 215or via external (e.g., manual) reporting, wireless stations 100 that areproximate (i.e., neighbors) to the identified wireless station 100-1(also referred to as the “main wireless station” 100-1) are examined forsimilar interference experiences (block 410). For example, interferencedetection system 215 may identify neighboring wireless stations 100within an initial distance from the main wireless site 100-1, based onthe geographic location of the main wireless site 100-1, the PRB(s) thatare experiencing the interference, and the timeframe(s) during which thePRB(s) experienced the interference. As described above, wirelessstation database 210 may include information regarding wireless stationsin RAN 205, such as location information (e.g., longitude and latitudeinformation) and performance metrics (e.g., PRB KPIs). Using thecollected information regarding wireless stations 100 in RAN 205,interference detection system 215 may ascertain the identities andlocations of neighboring wireless stations 100 that are experiencingsimilar interference during similar timeframes. For example, identifyingsimilar interference effects on neighboring wireless stations 100 mayinclude identifying this wireless stations 100 having similar uplinkinterference level measurements on the same PRBs during the same timeinterval as main wireless station 100-1.

FIG. 6 illustrates the main wireless station 100-1 and a number ofneighboring wireless stations 100-2, 100-3, 100-4, 100-5, and 100-6.Assume that main wireless station 100-1 has experienced interferencefrom an unknown source during at least some point in time. Consistentwith embodiments described herein, performance data for neighboringwireless stations 100-2 to 100-6 that exhibit a similar interference,may be obtained.

In some implementations, wireless stations 100-2 to 100-6, which mayexperience interference may be determined in an expanding step-wisemanner based on a location from main wireless station 100-1. Forexample, interference on neighboring wireless stations 100 may beinitially determined for neighboring wireless stations that are withindistances of about 3-4 kilometers (km) from the initial or main wirelessstation 100-1. For example, as shown in FIG. 6 , wireless stations100-2, 100-4, and 100-5 are within the initial range. If none of thestations are in the initial range, the range may be expandedincrementally, until a maximum range is reached. For example, the rangemay be expanded in 2 km increments until at least one other neighbor isdetermined or a maximum of 10 km from the main wireless station 100-1 isreached, though other smaller or larger increments are contemplatedherein. Neighbors at the shortest distance are more likely to experiencethe same interference as the main site and also offer data for enablingbetter accuracy when generating a heat map.

Referring back to FIG. 4 , after identifying neighboring wirelessstations 100, wireless stations 100 that are experiencing similarinterference effects are determined (block 415). As described above,external interference typically affects a small number of PRBs at awireless station 100. To filter out wireless stations that are notexperiencing the same interference, the PRB interference-related KPIdata (e.g., uplink signal level values) for the candidate wirelessstations 100 for the same time period as the main wireless station hasdetected interference, are retrieved and compared to the correspondinginterference-related KPI data on the affected PRBs. For example, usinguplink interference levels as an interference-related KPI, values in a−115 dBm to −120 dBm range generally indicate a low interference signal.In contrast, a high interference signal is usually indicated by anuplink interference level ranging from approximately −75 dBm to −105dBm. It should be noted that these ranges may be different, depending onthe environment and traffic each wireless station is handling.

By way of example, assume main wireless station 100-1 has identified anuplink interference level of −90 dBm on PRB 30 and an uplinkinterference level of −85 dBm on PRB 20, as indications of possibleinterference at PRBs 20 and 30. When identifying relevant neighbors,wireless stations having normal (e.g., −115 dBm to −120 dBm) uplinksignals for PRBs 20 or 30 are excluded or filtered out, even if thosewireless stations exhibit higher signals level on different PRBs. Tofocus the analysis on particular interference signals, data that mayindicate other possible interference signals or factors are excluded.For the following discussion, assume that wireless stations 100-2,100-4, and 100-6 are identified as experiencing interference on the samePRBs during the same timeframe as main wireless station 100-1.

After identifying neighboring wireless stations 100 as sites that mayhave experienced similar interference as main wireless station 100-1, ananalysis of the PRB data for those wireless stations is performed toidentify estimated locations for the source of the interference (block420). For example, to determine candidate interference source locations,path loss calculations, such as free space path loss (FSPL) calculationsmay be performed for each of a plurality of location approximationsbased on the distance between the wireless station and the selectedlocation approximation, the RF frequency of the PRB under investigation,and the estimated or expected uplink interference levels at the wirelessstation. Minimization calculations may be performed to increase theaccuracy of the obtained coordinates. For example, an indication of theaccuracy of the selected location approximation may be calculated foreach of the wireless stations experiencing interference based on theFSPL calculations and the actual observed uplink interference level, andthe interference source location approximation may be iterativelyadjusted until further adjustment does not result in an increased levelof accuracy. Although FSPL is provided as an exemplary path losscalculation methodology, it should be understood that additional methodsof path loss determination may also be used, consistent withimplementations described herein. Additional details regarding anexemplary process for determining candidate interference sourcelocations is provided below with respect to FIG. 7 .

Once candidate estimated interference source locations have beenidentified, scores are generated for each of the identified locations(block 425). For example, interference detection system 215 determines ascore for use in generating a heat map of possible interference sourcelocations briefly described above. In one exemplary implementation, thescores may be based on the RMSE values as well as a statistical constantreflecting the number or count of wireless stations that are possiblyexperiencing the interference. For example, each score may be weighted70% based on the number of wireless stations being analyzed and 30%based on the RMSE for the particular location.

Next, a heat map is generated based on the identified locations andtheir relative scores (block 430). For example, interference detectionsystem 215 generates a map that indicates the identified locations andprovides graphical indications of the probabilities that theinterference source is proximate to the identified locations.

The generated heat map may be used to ascertain the actual location ofthe interference source and to initiate remediation (block 435). Forexample, interference detection system 215 may provide or forward theheat map to interference reporting system 220 for delivery to relevantfield personnel or other entities associated with the service providerof RAN 205. In addition, as generally described above, an autonomousidentification system may use the generated heat map may to investigateand identify the interference source, without manual, humanintervention. For example, the autonomous identification system maydeploy one or more autonomous vehicles based on the generated heat map.In some embodiments, the deployed vehicles may include an RF spectrumanalyzer or similar measurement tools for locating and identifying thesource of RF interference. Once an interference source location isconfirmed, (e.g., when the automated vehicle identifies spikes in theinterference levels in the specific area it is driving/flying in) fieldpersonnel may be dispatched to communicate with the property owner toresolve the interference, if possible. In other implementations,automated messages or letters may be provided to relevant propertyowners, regarding eliminating/minimizing the interference.

FIG. 7 is a flow diagram illustrating one implementation of a process700 for determining candidate interference source locations consistentwith embodiments described herein. Process 700 may be performed byinterference detection system 215. However, in some embodiments, process700 may be performed by, or in conjunction with, one or more otherdevices or systems, such as wireless station database 210, and/orinterference reporting system 220.

Process 700 may include identifying the neighboring wireless station 100whose PRB data shows the highest interference (block 705). For clarity,the identified wireless station 100 may be referred to as the “strongestcorrelating station.” For example, using the information retrieved fromwireless station database 205, interference detection system 215 maycompare the uplink interference levels for the particular timeframeunder investigation for each of the wireless stations identified inblock 410. For example, as briefly described above, identifying similarinterference effects on neighboring wireless stations 100 may includeidentifying this wireless stations 100 having similar uplinkinterference level measurements on the same PRBs during the same timeinterval as main wireless station 100-1. The strongest correlatingstation may be identified as the neighboring wireless station whoseuplink interference levels most closely align with those of the mainwireless station.

As the result of the comparison, interference detection system 215 mayconclude that wireless station 100-1 is the strongest correlatingstation. The strongest correlating station may not be the main wirelessstation, since various factors may go into an initial identification ofan interference condition, for example the identification may be mademanually in response to customer complaints, effects on other networkequipment, diagnostics, etc.

Next, the geographic boundary for the likely interference sources isdetermined based on all possible combinations of the strongestcorrelating station with all other interference-affected neighbors(block 710), where each combination may correspond to a portion of theboundary. For example, if block 410 above identified threeinterference-affected wireless stations (100-1 (referred to as A), 100-2(referred to as B), 100-4 (referred to as C), and 100-6 (referred to asD)), with the strongest correlating station being wireless station A,the remaining combinations would include wireless station A-B, A-C, A-D,A-B-C, A-B-D, A-C-D, and A-B-C-D. FIG. 8 graphically depicts an exampleof such a boundary selection over the map of FIG. 6 . As shown, wirelessstations 100-1, 100-2, 100-4, and 100-6 form the vertices along theouter boundary 800 within which the interference source is likely to befound.

Next, an initial interference source location within the geographicboundary is selected (block 715). An exemplary location is depicted inFIG. 8 at location 810-1, within boundary 800. In some implementations,an initial location may be set equal to the location of the strongestcorrelating station, although any other location with boundary 600 maybe selected.

Using the selected location, expected interference-related KPI valuesfor each wireless station on each combination from the initiallyselected location are determined (block 720). For example, expecteduplink interference signal level values may be calculated using freespace path loss as expressed by equations (1) and (2) below:

$\begin{matrix}{{d = {10^{{{({{20{\log_{10}({frequency})}} - {Si{gnal}{Level}} - {2{7.5}5}})}/2}0}}},} & (1)\end{matrix}$

where d is the distance between wireless station and the selectedlocation (in km), frequency refers to the RF frequency (in megahertz) ofthe PRB under investigation, SignalLevel refers to the estimated orexpected uplink interference signal level value at the wireless station(in decibel-milliwatts), and 27.55 is a constant relating to thespherical wave front of the RF signal and the units selected for thecomputation (e.g., km and MHz) in this example. The distances betweenthe selected location and the respective wireless stations A-D aredepicted as d_(A) to d_(D) in FIG. 8 . Solving equation (1) forSignalLevel results in:

SignalLevel=20 log₁₀(frequency)+20 log₁₀(d)−27.55  (2)

Once expected values for uplink interference levels have been calculatedfor each wireless station 100, these values are compared to the observedor actual values to determine the accuracy of the selected location(block 725). In one implementation, the comparison may includecalculating root mean squared error for each interference experiencingwireless station. The root mean squared error may be expressed as:

RMSE=√{square root over ([Σ_(i=1)^(n)(Expected_(i)−Actual_(i))²)}/n],  (3)

where n is the number of interference-experiencing wireless stations,expected is the uplink interference level calculated in equation (2),and actual is the observed uplink interference level at the time of theinterference, whose value was retrieved from wireless station database210 at block 410 above. A lower value for RMSE indicates that theexpected value is closer to the actual value over the range of data.Although RMSE is described as an accuracy determining methodologyherein, other statistical calculations for error may be used, such asmean square error (MSE), mean absolute scaled error (MASE), meanabsolute percentage error (MAPE), symmetric MAPE (SMAPE), etc.

A minimization process is performed for RMSE (block 730). For example,interference detection system 215 may iteratively select additionalestimated locations and calculate expected and RMSE values for eachlocation, until a minimum RMSE is obtained. Process 700 may result indetermining a number of locations and their corresponding RMSE values.

FIG. 9 is an example of a heat map 900 generated using the process ofblock 430. As shown, heat map 900 includes a geographical map of theaffected locations and identifies the specific locations 810-1 through810-x. In addition, heat map 900 includes graphical indicia 905 based onthe relative scores and aggregate proximity for each location thatindicates a relative probability that the interference source would befound in a particular area. In some embodiments, as shown in FIG. 9 ,graphical indicia 905 may be provided as an overlay of varying color oropacity to indicate higher and lower probability.

In the embodiments described above, sector azimuth (i.e., angle oforientation of the antenna) and beam width are not taken intoconsideration in identifying a possible interference source. This may bethe case for wireless stations that broadcast omnidirectional signalshaving a beam width of 360 degrees. However, in some circumstances,particular sectors of wireless stations may transmit signals indifferent, selected directions. To account for the antenna directions,the FSPL determined at block 420 may be adjusted by identifyingboundaries (e.g., polygons) for each one of the sectors for eachwireless station. The FSPL calculation may then be adjusted based onwhether a guessed location falls within the boundary for the particularsector. If the guessed location is within the boundary, no adjustmentsare necessary. However, if the point is not within the boundary, anadjustment is made to the FSPL calculation for the particular location.For example, a +3 dB adjustment may be made to reflect that theparticular wireless station is not detecting the interference directlywithin its transmission beam and detects a lower level of interferencethan the one calculated without the adjustment.

FIG. 10 illustrates a portion of FIG. 8 in which wireless station 100-1cover sectors 1000-1 to 1000-3. Sector 1000-2 is under investigation forthe PRBs discussed above and is shaded in gray. In this example, severalof locations 810 are not within the boundaries of sector 1000-2.Consistent with embodiments described herein, the FSPL calculations forthese guessed locations may be adjusted by +3 dB for each wirelessstation, for which the location falls outside of the sector boundary. Inthis way, lower interference values for locations outside of aparticular sector do not unnecessarily impact the RMSE minimizationprocess. The adjustments may result in more accurate locationdetermination.

In some instances, various portions of the heat map may have similarintensities (based on the scores generated in block 425 above),rendering it difficult to identify a particular field search startingpoint without field expertise or any additional information. Consistentwith embodiments described herein, a collocated sector analysis may beperformed to estimate a direction in which the interference source ismore likely to be located on a per-wireless station basis.

FIG. 11 illustrates an example process 1100 for determining a startingsearch location based on a collocated sector analysis. In someembodiments, process 1100 may be performed by interference detectionsystem 215. In some embodiments, process 1100 may be performed by, or inconjunction with, one or more other devices or systems, such as wirelessstation database 210, and/or interference reporting system 220. FIG. 11is described in conjunction with FIG. 10 .

In addition to, or in lieu of the heat map described above (e.g., heatmap 900), a cluster map may be generated (block 1105) that clusters thepossible interference source locations based on a predetermined clusterdistance (e.g., 800 meters). Next, it is determined whether multipleclusters have been identified (block 1110). For example, based onK-means clustering, various clusters and related cluster centroids maybe generated. If optimized clustering results in a single cluster beingidentified (block 1110—NO), field searching may be targeted based on thecentroid of the identified cluster (block 1115). However, if multipleclusters are identified (block 1110—YES), an initial sector analysisvector may be generated for each wireless station 100 that isexperiencing similar interference effects (block 1120).

As described above in relation to FIG. 10 , each wireless station 100that is experiencing similar interference effects (as identified inblock 415 above) may have more than one sector pointing in differentdirections (azimuth) and with different beam widths. Taking this intoaccount, it can be assumed that, like the embodiment of FIG. 10 , anysector that is seeing the interference source directly (i.e., within itsazimuth and beam width) will most likely show a higher interferencepower level. Accordingly, for a site that has more than one sectoraffected by interference, a vector can be initially determined in thedirection of the most affected sector's azimuth (e.g., that sectorshowing the highest interference power level; also referred to as the“main” sector). In the example, of FIG. 10 , this vector 1050 (shown asa dashed line) would be directed along the azimuth angle of sector1000-2, which may be referred to as the main sector.

Next, consistent with implementations described herein, the angle of theinitial sector analysis vectors may be steered (i.e., adjusted) based onthe interference power levels on the collocated sectors (block 1125).For example, consider wireless station wireless station 100-1 havingsectors 1000-1 to 1000-3, as shown in FIG. 10 . As discussed above, eachsector 1000-1 to 1000-3 has a particular interference power reading fora particular PRB that is experiencing interference effects. A resultantvector 1055 may be generated based on the azimuth, beam width, andinterference power level in the main sector (the one with the highestreading) as well as each of the other sectors. In one implementation,the angle adjustment from the main sector azimuth angle is based on aratio of the main sector interference power level to each of theremaining sector power levels, which may be referred to as the intensityratio for each of the remaining sectors.

Using the sector example of FIG. 10 , assume that for PRB 40 underanalysis, sector 1000-2 is the main sector and has an azimuth of 0° anda beam width of 120° and an interference power level of −83 dBm, sector1000-3 has an interference power level of −102 dBm, and sector 1000-1has an interference power level of −90 dBm. Using this information, anintensity ratio of 1.23 is calculated for sector 1000-3 (−102/083) andan intensity ratio of 1.08 is calculated for sector 1000-1 (−90/−83).The angle of adjustment (denoted as 0 in FIG. 10 ) may be calculatedusing the difference between the intensity ratios for sectors 1000-3 and1000-1, which is 0.15 in this case (1.23-1.08). This difference inratios is then multiplied with beam width of main sector 1000-2 (120°),resulting in an angle adjustment of 18° (120×0.15) toward sector 1000-1,as represented by adjusted vector 1050 in FIG. 10 . Note that if sector1000-3 had a higher intensity ratio than sector 1000-1, the differencewould be negative 0.15, which would result in a −18° angle adjustmenttoward sector 1000-3.

Once the sector analysis vectors have been adjusted for all wirelessstations experiencing interference effects, the vectors may be applied(e.g., overlaid) on the heat map and/or cluster map to help target alikely interference source from among a number of candidate locations orclusters (block 1130).

FIG. 12 illustrates an example process 1200 for further refiningpredicted interference locations using historical data, consistent withimplementations described herein. Such historical refinement allows forfluctuations in interference-related KPI data for affected PRBs that maycause corresponding fluctuations in the output of processes 400, 700,and 1100 described above. In some embodiments, process 1200 may beperformed by interference detection system 215. In some embodiments,process 1200 may be performed by, or in conjunction with, one or moreother devices or systems, such as wireless station database 210, and/orinterference reporting system 220.

Process 1200 may begin by identifying likely persistent or constantinterference sources, referred to as a persistent interferers (block1205). For example, interference detection system 215 may retrieveinterference information, such as that determined and generated inprocess 400 described above, for a predetermined time interval, such asa three-day period. For example, process 400 described above may beperformed on a daily basis using, for example, PRB information fromaffected wireless stations 100. The collected information may be used todetermine or identify persistent interferers, that is, likelyinterference sources that induce interfering effects on one or morewireless stations consistently over the period of time.

Once one or more possible persistent interferers has been identified,information regarding the predicted locations for the interferencesource are retrieved or collected for the selected period of time (block1210). Consistent with implementations described herein, results ofperiodic (e.g., daily) interference source location processing (e.g.,processes 400) may be collected or aggregated, such as in a database orsimilar storage, for subsequent retrieval. In block 1210, the predictedinterference source locations for each stored interval (e.g., day) maybe retrieved for a selected interval (e.g., a particular three-dayperiod, etc.).

As described above, predicted interference source locations for aparticular interference incidence are determined based on scores foreach of a number of possible interference source locations (e.g.,resulting from block 425 described above). Because process 400 describedabove identifies a number of multiple possible locations, each having ascore, in some implementations, a singular predicted location for aparticular incident period (e.g., a single day) may be determined usinga centroid(s) of the cluster(s) of possible locations. For example, aweighted centroid may be calculating based on the scores of the possiblelocations used in process 400. In other embodiments, a simple centroidof the estimated locations (i.e., unweighted) that have scores above apredefined threshold may be calculated to represent the singularpredicted location for the particular incident period. In still otherimplementations, rather than basing the singular predicted location onthe cluster of estimated locations, a highest scoring location may beidentified as the singular predicted location for the particularincident period.

Next, it is determined whether multiple interference source locationshave been identified (block 1215). For example, in some implementations,it is possible that more than one persistent interferer are causing theidentified interference effects. This may be indicated when processes400/1100 identify multiple scored clusters, each having a uniquecentroid or singular predicted location. As described above, eachidentified cluster may correspond to a predicted interference sourcelocation for the particular interval (e.g., day).

If it is determined that only a single predicted interference sourcelocation has been identified (block 1215—NO), an aggregated predictedinterference source location is calculated based on the predictedinterference locations for each interval (e.g., day) in the selectedhistorical time period (e.g., 30 days) (block 1220). Consistent withimplementations described herein, an exemplary methodology forcalculating an aggregated predicted interference source locationincludes k-means clustering using each of the retrieved singularpredicted locations, wherein k is set equal to 1. For example, acentroid location identified as singular predicted interference sourcelocation for each day in the three-day time period may be used togenerate a single aggregated predicted interference source locationcorresponding to the centroid of the cluster of daily results.

If it is determined that multiple interference source locations havebeen identified (block 1215—YES), multiple aggregated predictedinterference source locations are calculated based on the multiplepredicted interference locations for each interval (e.g., day) in theselected historical time period (e.g., 3 days) (block 1225). Forexample, k-means clustering may be performed in which the value of k isset equal to the discrete number of interference source locations thathave been identified for each day. For example, if two clusters wereidentified for a particular interference incident, as reflected by theretrieved information including two, discrete, singular predictedinterference locations (e.g., centroids of the clusters), k is set equalto 2, and the k-means processing results in two aggregated predictedinterference source locations.

Once the one or more aggregated predicted interference source locationsare identified, field searching may be targeted based on the resultinglocation(s) (block 1230).

FIG. 13 illustrates an example process 1300 for refining the aggregatedpredicted interference source location from process 1200 usinginformation associated with one or more small cell wireless stations,consistent with implementations described herein. As with the processesdescribed above, process 1300 may be performed by interference detectionsystem 215, alone or in conjunction with one or more other devices orsystems, such as wireless station database 210, and/or interferencereporting system 220.

In some implementations, RAN 205 may include a particular type orcategory of wireless stations 100 that are referred to as “small cells.”In general, small cell wireless stations refer to wireless stationshaving an antenna volume of less than three cubic feet, and which aremounted 50 feet or less in height relative to the height of a structureon which it is mounted, or the ground if the wireless station is freestanding. In addition, small cell wireless stations generally have ashorter range, due to their lower height and decreased antenna volume.Although variations exist, many small cell wireless stations includeomnidirectional antennas.

Consistent with implementations described herein, process 1300 mayidentify small cell wireless stations within a predetermined distancefrom the aggregated predicted interference source location calculated inprocess 1200 (block 1305). For example, a distance of approximately 2-3miles may provide a suitable distance for identifying affected smallcell wireless stations. For example, interference detection system 215may retrieve locations of small cell wireless stations from wirelessstation database 210 and may compare the retrieved locations to theaggregated predicted interference source location from process 1200.

At block 1310, the process determines whether at least two of theidentified small cell wireless stations are affected by the interferer.For example, for any identified small cell wireless stations within thepredetermined distance, interference detection system 215 may determinewhether it has been affected by the interference under analysis. Forexample, as described above in relation to process 400, affectedwireless stations associated with an interference event are identifiedat block 410.

If it is determined that two or more such small cell wireless stationsare not within the predetermined distance of the aggregated predictedinterference source location (block 1310—NO), process 1300 terminates.However, if it is determined that two or more such small cell wirelessstations are within the predetermined distance from the aggregatedpredicted interference source location (block 1310—YES), the locationand PRB data for each location are retrieved (e.g., from wirelessstation database 210) (block 1315) and an estimated distance from eachof the identified small cell wireless stations to the interferencesource is calculated (block 1320). For example, an FSPL calculation maybe performed for each of the two or more small cell wireless stationlocations based on, in some implementations, the RF frequency of the PRBunder investigation, and, for example, and average uplink interferencelevel value at the small cell wireless station over the particular timeinterval (e.g., 3 days) under investigation. Based on the estimateddistance values for each small cell wireless station, a refinedpredicted location of the interference source is determined (block1325).

For example, consistent with implementations described herein, atrilateration approach may be used to identify the refined predictedlocation of the interference source. FIGS. 14A and 14B are graphicaldepictions of a trilateration methodology consistent withimplementations described herein. FIG. 14A depicts trilateration for twosmall cell wireless stations and FIG. 14B depicts trilateration forthree small cell wireless stations.

As shown in FIG. 14A, small cell wireless stations 100SC-1 and 100SC-2include relative locations (x1,y1) and (x2,y2), which may identifyspecific geographic coordinates. FSPL is used to calculate the estimateddistance from the interference source to each small cell wirelessstation, as depicted by distances 1405 and 1410, respectively. Circles1415 and 1420 with radii corresponding to distances 1405 and 1410,respectively, depict the predicted location of the interference sourcefor each small cell wireless station. The area of intersection 1425between circles 1415 and 1420 generally depicts the refined predictedlocation of the aggregated interference source. As an additionalrefinement, in some implementations, the discrete points of intersection1430 a and 1430 b between circles 1415 and 1420 may be furtheridentified as possible interference source locations.

FIG. 14B includes a third small cell wireless stations 100SC-3 having arelative location (x3,y3). As in FIG. 14A, FSPL is used to calculate theestimated distance 1435 from the interference source to small cellwireless station 100SC-3. Circle 1440 having a radius corresponding todistance 1435 depicts the predicted location of the interference sourcefor small cell wireless station 100SC-3. The area of intersection 1445between all three circles, 1415, 1420, and 1440 depicts the refinedpredicted location of the aggregated interference source.

Although process 1300 is described above with respect to aggregatedinterference source location determine in process 1200, in otherembodiments, process 1300 may be applied to an output of one or more ofprocesses 400 and 1100, to refine a predicted interference sourcelocation for an acute interference incident.

The foregoing description of implementations provides illustration anddescription but is not intended to be exhaustive or to limit theinvention to the precise form disclosed. Modifications and variationsare possible in light of the above teachings or may be acquired frompractice of the invention. For example, while series of blocks andsignal messages have been described with respect to FIGS. 5 and 7 , theorder of the blocks and signal messages may be varied in otherimplementations. Moreover, non-dependent blocks may be performed inparallel.

Certain features described above may be implemented as “logic” or a“unit” that performs one or more functions. This logic or unit mayinclude hardware, such as one or more processors, microprocessors,application specific integrated circuits, or field programmable gatearrays, software, or a combination of hardware and software.

No element, act, or instruction used in the description of the presentapplication should be construed as critical or essential to theinvention unless explicitly described as such. Also, as used herein, thearticle “a” is intended to include one or more items. Further, thephrase “based on” is intended to mean “based, at least in part, on”unless explicitly stated otherwise.

To the extent the aforementioned embodiments collect, store, or employpersonal information of individuals, it should be understood that suchinformation shall be collected, stored, and used in accordance with allapplicable laws concerning protection of personal information.Additionally, the collection, storage, and use of such information canbe subject to consent of the individual to such activity, for example,through well known “opt-in” or “opt-out” processes as can be appropriatefor the situation and type of information. Storage and use of personalinformation can be in an appropriately secure manner reflective of thetype of information, for example, through various encryption andanonymization techniques for particularly sensitive information.

In the preceding specification, various preferred embodiments have beendescribed with reference to the accompanying drawings. It will, however,be evident that various modifications and changes may be made thereto,and additional embodiments may be implemented, without departing fromthe broader scope of the invention as set forth in the claims thatfollow. The specification and drawings are accordingly to be regarded inan illustrative rather than restrictive sense.

What is claimed is:
 1. A method, comprising: determining that an unknownradio frequency (RF) interference source that causes RF interferenceexperienced by a first wireless station is a persistent RF interferencesource over a plurality of time intervals in a selected time period;identifying a predicted interference source location for each timeinterval in the selected time period; and calculating an aggregatedpredicted interference source location based on the identified one ormore predicted interference source locations.
 2. The method of claim 1,wherein identifying the predicted interference source locationcomprises: determining a plurality of estimated interference sourcelocations based on at least geographic locations of the first wirelessstation and one or more second wireless stations, proximate to the firstwireless station, that have also experienced interference; comparing,for each of the plurality of estimated interference source locations,estimated interference to observed interference at the one or moresecond wireless stations; and identifying the predicted interferencesource location based on the comparing.
 3. The method of claim 2,further comprising: identifying one or more clusters based on thecomparing of the estimated interference to observed interference at eachof the one or more second wireless stations; and identifying thepredicted interference source location based on the one or moreidentified clusters.
 4. The method of claim 3, wherein the predictedinterference source location is identified based on a centroid of theone or more identified clusters.
 5. The method of claim 2, whereincalculating the aggregated predicted interference source location basedon the identified predicted interference source locations comprises:performing k-means clustering based on the identified predictedinterference source locations.
 6. The method of claim 5, furthercomprising: identifying a number of clusters associated with theidentified predicted interference source locations, wherein more thanone cluster is indicative of more than one possible unknown interferencesource; and performing the k-means clustering of the identifiedpredicted interference source locations based on the identified a numberof clusters.
 7. The method of claim 6, further comprising: setting avalue of k equal to the number of identified clusters.
 8. The method ofclaim 2, further comprising: identifying one or more second wirelessstations that are small cell wireless stations; calculating estimateddistances between to the unknown RF interference source and each of theidentified small cell wireless stations; and identifying a refinedpredicted interference source location based on the calculated estimateddistances and relative locations of the identified small call wirelessstations.
 9. The method of claim 8, further comprising: identifyingsmall cell wireless stations that are within a predetermined distancefrom the aggregated predicted interference source location; calculatingestimated distances between the unknown RF interference source and eachof the identified small cell wireless stations that are within thepredetermined distance from the aggregated predicted interference sourcelocation; and identifying the refined predicted interference sourcelocation based on the calculated estimated distances and relativelocations of the identified small call wireless stations that are withinthe predetermined distance from the aggregated predicted interferencesource location.
 10. The method of claim 8, wherein calculating theestimated distances comprises performing free space path loss (FSPL)calculations for each of the identified small cell wireless stations.11. The method of claim 2, wherein the RF interference comprises RFinterference on at least one physical resource block (PRB) on the firstwireless station, and wherein identifying the one or more secondwireless stations comprises identifying one or more second wirelessstations that have experienced similar interference on the at least onePRB.
 12. The method of claim 11, wherein identifying the first wirelessstation comprises determining that a key performance indicator (KPI) forthe at least one PRB on the first wireless station has a valueindicative of interference.
 13. A network device, comprising: aprocessing unit configured to: determine that an unknown radio frequency(RF) interference source that causes RF interference experienced by afirst wireless station is a persistent RF interference source over aplurality of time intervals in a selected time period; identify apredicted interference source location for each time interval in theselected time period; and calculate an aggregated predicted interferencesource location based on the identified one or more predictedinterference source locations.
 14. The network device of claim 13,wherein the processing unit configured to identify the predictedinterference source location is configured to: determine a plurality ofestimated interference source locations based on at least geographiclocations of the first wireless station and one or more second wirelessstations, proximate to the first wireless station, that have alsoexperienced interference; compare, for each of the plurality ofestimated interference source locations, estimated interference toobserved interference at the one or more second wireless stations; andidentify the predicted interference source location based on thecomparing.
 15. The network device of claim 14, wherein the processingunit is further configured to: perform k-means clustering based on theidentified predicted interference source locations.
 16. The networkdevice of claim 15, wherein the processing unit is further configuredto: identify a number of clusters associated with the identifiedpredicted interference source locations, wherein more than one clusteris indicative of more than one possible unknown interference source; andperform the k-means clustering of the identified predicted interferencesource locations based on the identified a number of clusters.
 17. Thenetwork device of claim 14, wherein the processing unit is furtherconfigured to: identify one or more second wireless stations that aresmall cell wireless stations; calculate estimated distances between theunknown RF interference source and each of the identified small cellwireless stations; and identify a refined predicted interference sourcelocation based on the calculated estimated distances and relativelocations of the identified small call wireless stations.
 18. Thenetwork device of claim 17, wherein the processing unit is furtherconfigured to: identify small cell wireless stations that are within apredetermined distance from the aggregated predicted interference sourcelocation; calculate estimated distances between to the unknown RFinterference source and each of the identified small cell wirelessstations that are within the predetermined distance from the aggregatedpredicted interference source location; and identify the refinedpredicted interference source location based on the calculated estimateddistances and relative locations of the identified small call wirelessstations that are within the predetermined distance from the aggregatedpredicted interference source location.
 19. The network device of claim18, wherein the processing unit configured to calculate the estimateddistances between to the unknown RF interference source and each of theidentified small cell wireless stations that are within thepredetermined distance from the aggregated predicted interference sourcelocation is further configured to: perform free space path loss (FSPL)calculations for each of the identified small cell wireless stations.20. A non-transitory storage medium storing instructions executable by anetwork device, wherein the instructions comprise instructions to causethe network device to: determine that an unknown radio frequency (RF)interference source that causes RF interference experienced by a firstwireless station is a persistent RF interference source over a pluralityof time intervals in a selected time period; identify a predictedinterference source location for each time interval in the selected timeperiod; and calculate an aggregated predicted interference sourcelocation based on the identified one or more predicted interferencesource locations.